Automation is the key

• The package “devtools”

Automation is the key

devtools has undergone a conscious uncoupling to split out functionality into packages:

• testthat: Writing and running tests (i.e. test()).

• roxygen2: Function and package documentation (i.e. document()).

• remotes: Installing packages (i.e. install_github()).

• usethis: Automating package setup (i.e. use_test()).

• …

• … and many more.

Generally, you would not need to worry about these different packages, because devtools installs all of them automatically.

Basic components

Package component

DESCRIPTION

• Metadata (include authors, dependencies, versions, etc.)

NAMESPACE

• Namespace (document the function names in your package, to avoiding name space overlap with other packages)

R/

• A directory containing all the code, with each R script as a function.

man/

• A directory containing object documentation.

On the journey

Well-proportioned code

Your code is executed by machines but read by human who will check and build upon it.

1. Regular spacing between elements

2. Not too wide

3. Not too long

4. Not too stained: just the right amount of comments

Regular spacing

🐈

starwars%>%
  select(name,height, mass,homeworld) %>%
  mutate(
    mass= NULL,
    height =height *0.0328084 # convert to feet
  )

🦁

starwars %>%
  select(name, height, mass, homeworld) %>%
  mutate(
    mass = NULL,
    height = height * 0.0328084 # convert to feet
  )

Regular spacing (con’d)

Just to name a few

• Always put a space after a comma, never before, just like English

a <- c("Alpine", "Badger", "Cougar", "Dissolve")

• Most infix operators (==, +, -, <-, etc.) should always be surrounded by spaces:

a <- 5
b <- 8

c = a + b

How to have a good habit?

• Learn from standard: the tidyverse style guide

• Package styler does it automatically for you! (demo)

• In RStudio IDE, Ctrl + I for indentation

Not too wide

🐈

a <- data %>% filter(species == "cougar") %>% group_by(age) %>% summarize(mean_weight = mean(weight))

🦁

a <- data %>% 
  filter(species == "cougar") %>% 
  group_by(age) %>% 
  summarize(mean_weight = mean(weight))

How to have a good habit?

• No more than 80 characters per line, or something similar.

• Package linter can help you with this! (demo)

• In RStudio IDE, use Global Options > Code > Display > Show Margin

Not too long

• The same as writing - one paragraph for one idea

• Make good use of empty line

• In RStudio IDE, use Ctrl + Shift + R to insert sections and outline (demo)

• Code efficiently!

🐈 reinvent the wheel

default_values <- list(a = 1, b = 2)
options <- list(b = 56)
temporary_list <- default_values
temporary_list[names(options)] <- options
options <- temporary_list

options

$a
[1] 1

$b
[1] 56

Not to long

🦁 Much better by using modifyList function

default_values <- list(a = 1, b = 2)
options <- list(b = 56)
options <- modifyList(default_values, options)
options

$a
[1] 1

$b
[1] 56

How to extend R vocabulary?

• Read other people’s code (colleague, workshop, blog post, etc.)

• Ask others to read your code

• Internet!

Not too stained

As few comments as possible

Some people might do this…

# starwars data
starwars %>%
  # select name and mass
  select(name, mass) %>%
  mutate(
    # add mass2 as double of mass
    mass2 = mass * 2,
    # add mass2_squareed as squared mass2
    mass2_squared = mass2 * mass2
  )

Too many comments might be dangerous!

Well-proportioned code

Your code is executed by machines but read by human who will check and build upon it.

1. Regular spacing between elements

2. Not too wide

3. Not too long

4. Not too stained: just the right amount of comments

Other resources about R

• A talk Code Smells and Feels by Jenny Bryan

• R Open Sci

• RLadies

• Posit annual conference

• International R user conference

Concept of a project

If the first line of your R script is setwd("C:\Users\jenny\path\that\only\I\have") I will come into your office and SET YOUR COMPUTER ON FIRE 🔥. - Jenny Bryan

• Have you ever received a R script from a friend with this line

• And it’s 0% chance that you can run this setwd() without an error

• If you never heard about setwd()

• Don’t worry, you don’t need to know 😄

Concept of a project

A folder on your computer that holds all the files relevant to that particular piece of work.

• Get your files organized

• Your work is reproducible

• There are two things that can help us creating a self-contained project

1. R project

A self-contained working environment with folders for data, scripts, outputs, etc

2. here package

When import/export data is required, filepaths are written relative to the root folder of the R project.

R project: how to use

1. Open R Studio
2. Select File > New Project > New Directory > provide name and where you want it to be saved
3. The R project you create will come in as a folder containing a .Rproj file
4. This is a shortcut and likely the primary way you will open your project
5. Your R Studio top-right corner would show the project you are in

here package: how to use

1. It’s a good practice to structure your working directory in a consistent way

   • ./data: to include all the data

   • ./docs: to include none data files such as manuscript

   • ./R: R script

2. Load the package by running library(here)

3. here would set the root folder as the folder you have your .Rproj file

4. Then you will only need relative path whenever you need to import data

Use relative path, not absolute path to include data

library(here) 
my_data <- read_csv(here("data", "my_data.csv"))